Local Density Encoding for Robust Stereo Matching

Vinh Dinh Nguyen,Duc Dung Nguyen,Sang Jun Lee,Jae Wook Jeon IEEE 2014 IEEE transactions on circuits and systems for vide Vol.24 No.12

<P>Stereo correspondence is challenging under realistic conditions due to uncontrolled factors that affect input images, including illumination inconsistencies and radiometric variations. Many local and global models have been suggested to address these problems; however, their performance is often degraded due to the assumption of color consistency between the left and right images. Therefore, we present a new local pattern, local density encoding, for stereo matching measurements to improve the performance of existing stereo methods. Our experimental results indicate that the proposed method is less sensitive to illumination changes and radiometric variations. Moreover, in the cases with normal and severe illumination changes, the proposed method is more robust than state-of-the-art data costs.</P>

BONEcheck: A digital tool for personalized bone health assessment

Dinh Tan Nguyen,Thao P. Ho-Le,Liem Pham,Vinh P. Ho-Van,Tien Dat Hoang,Thach S. Tran,Steve Frost,Tuan V. Nguyen 대한골다공증학회 2023 Osteoporosis and Sarcopenia Vol.9 No.3

Objectives: Osteoporotic fracture is a significant public health burden associated with increased mortality risk and substantial healthcare costs. Accurate and early identification of high-risk individuals and mitigation of their risks is a core part of the treatment and prevention of fractures. Here we introduce a digital tool called 'BONEcheck' for personalized assessment of bone health. Methods: The development of BONEcheck primarily utilized data from the prospective population-based Dubbo Osteoporosis Epidemiology Study and the Danish Nationwide Registry. BONEcheck has 3 modules: input data, risk estimates, and risk context. Input variables include age, gender, prior fracture, fall incidence, bone mineral density (BMD), comorbidities, and genetic variants associated with BMD. Results: Based on the input variables, BONEcheck estimates the probability of any fragility fracture and hip fracture within 5 years, subsequent fracture risk, skeletal age, and time to reach osteoporosis. The probability of fracture is shown in both numeric and human icon array formats. The risk is also contextualized within the framework of treatment and management options on Australian guidelines, with consideration given to the potential fracture risk reduction and survival benefits. Skeletal age was estimated as the sum of chronological age and years of life lost due to a fracture or exposure to risk factors that elevate mortality risk. Conclusions: BONEcheck is an innovative tool that empowers doctors and patients to engage in wellinformed discussions and make decisions based on the patient's risk profile. Public access to BONEcheck is available via https://bonecheck.org and in Apple Store (iOS) and Google Play (Android).

A Fast Evolutionary Algorithm for Real-Time Vehicle Detection

Vinh Dinh Nguyen,Thuy Tuong Nguyen,Dung Duc Nguyen,Sang Jun Lee,Jae Wook Jeon IEEE 2013 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY Vol.62 No.6

<P>The evolutionary algorithm (EA) is an effective method for solving various problems because it can search through very large search spaces and can quickly come to nearly optimal solutions. However, existing EA-based methods for vehicle detection cannot achieve high performance because their fitness functions depend on sensitive information, such as edge or color information on the preceding vehicle. This paper focuses on improving the performance of existing evolutionary-based methods for vehicle detection by introducing an effective fitness function that can more accurately capture a vehicle's information by combining a disparity map, edge information, and the position and motion of the preceding vehicle. The proposed method can detect multiple vehicles by using a turn-back genetic algorithm (GA) and can prevent false detection by using motion detection. Our fitness function is designed in a typical manner along with the fitness parameters. These parameters are usually selected using heuristic methods, making the choice of optimal parameters difficult. Therefore, this paper proposes a new approach to estimating optimal fitness parameters using EA and the least squares method. Robustness testing showed that the proposed method provides detection rate (DR) results close to those obtained using a state-of-the-art system and outperforms other dominant vehicle-detection-based EAs.</P>

Lithium Chloride-Imidazolium Chloride Melts for the Coupling Reactions of Propylene Oxide and CO₂

Nguyen, Ly Vinh,Lee, Bo-Ra,Nguyen, Dinh Quan,Kang, Min-Jung,Lee, Hyun-Joo,Ryu, Seol-Ryu,Kim, Hoon-Sik,Lee, Je-Seung Korean Chemical Society 2008 Bulletin of the Korean Chemical Society Vol.29 No.1

A series of lithium chloride-imidazolium chloride (LiCl-[imidazolium]Cl) melts were prepared and their catalytic activities were evaluated for the coupling reactions of propylene oxide and CO2. At the constant mole of LiCl, the catalytic activities of LiCl-[imidazolium]Cl melts increased with increasing molar ratio of [imidazolium]Cl/LiCl up to 2, but thereafter decreased rapidly. The variation of alkyl groups on the imidazolium ring showed a negligible effect on the catalytic activity, but the number of alkyl groups present on the imidazolium cation exerts a pronounced effect. Catalysis and electrospray ionization tandem mass spectral analysis results of LiCl-[imidazolium]Cl melts imply that the activity of the melt is strongly related to the amount of LiCl2- generated from the melt.

Real-Time Vehicle Detection Design and Implementation on GPU

Vinh Dinh Nguyen,Thuy Tuong Nguyen,Dung Duc Nguyen,Jae Wook Jeon 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10

Vehicle detection and distance estimation system has become important due to their assistance in reducing vehicle accidents. Therefore, an efficient vehicle detection and distance estimation algorithm using a knowledge-based method and image segmentation technique has been developed. The proposed algorithm can detect and estimate the distance of the preceding vehicle under various road conditions using a single CCD camera of 16 mm and 25 mm focal lenghts mounted on a vehicle. A GPU implementation of this proposed algorithm is introduced to enable our proposed system to support real-time processing. Experimental results under various road and weather conditions prove that our proposed system is suitable for a real-time system.

Local Stereo Matching Using an VariableWindow, Census Transform and an Edge-preserving Filter

Vinh Quang Dinh,Dung Duc Nguyen,Vinh Dinh Nguyen,Jae Wook Jeon 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10

In this paper, we propose an alternative blocking-matching approach to the correspondence problem in stereo matching. In blocking-matching algorithms, a local window is used to measure the similarity (or dissimilarity) between pixels of a stereo pair. Although some area-based stereo matching methods have been developed and work well in many kinds of regions such as textureless or object boundary regions, their performance can degrade when working in some types of radiometric conditions. Our proposed algorithm is an improved method that uses a non-parametric transform in the pre-processing step and an edge-preserving filter in the post-processing step. Input images are first pre-processed by the census transform, which makes the proposed method more robust when the image pair is captured in different light sources or camera exposure conditions. The window cost in our approach is computed from the transformed images using the Hamming distance, and the correspondence is finally chosen by a Winner-Takes-All strategy. The experimental results for the Middleburry images show that the proposed method outperforms test local stereo methods in radiometrically different images.

Robust Stereo Data Cost With a Learning Strategy

Nguyen, Vinh Dinh,Nguyen, Hau Van,Jeon, Jae Wook IEEE 2017 IEEE transactions on intelligent transportation sy Vol.18 No.2

<P>The performance of stereo matching algorithms strongly depends on the quality of the stereo data/matching cost. Most state-of-the-art data costs require expert knowledge for the design of a transformation function, such as census for handling gray-level changes monotonically, adaptive normalized cross correlation for handling Lambertian cases, guided filtering for preserving edge information, and local density encoding for handling illumination differences. However, it is difficult to design a complex transformation function to handle unknown factors that often occur in driving conditions such as snow, rain, and sun. Therefore, this paper has investigated the deep learning strategy to develop a novel stereo matching cost model without using much expert knowledge. Experimental results show that the proposed deep learning model obtains better results than the state-of-the-art stereo matching cost as judged by the standard KITTI benchmark, Middlebury, and HCI datasets.</P>

Fuzzy Encoding Pattern for Stereo Matching Cost

Dinh, Vinh Quang,Nguyen, Vinh Dinh,Van Nguyen, Hau,Jeon, Jae Wook IEEE 2016 IEEE Transactions on Circuits and Systems for Vide Vol.26 No.7

<P>We propose a novel fuzzy encoding pattern that fuzzily encodes the relative orders between pixel pairs. An image window is divided into disjoint neighboring pixel sets for the window's center pixel, and the relative order is established not only between the center pixel and its neighbors but also between the pixel pairs in each neighboring pixel set. The relative orders are fuzzily encoded to extract more detailed information from a local structure. We successfully apply the pattern as a matching cost function for stereo correspondence under severe radiometric variations. We conduct experiments using the proposed matching cost function and compare it with functions employing the census transform, supporting local binary pattern, and adaptive normalized cross correlation, as well as a mutual information-based matching cost function, using different stereo data sets. Compared with the census transform, the proposed function reduces the error from 33.1% to 16.9% in the Middlebury data set and from 17.6% to 9.5% in the Kitti data set. The experimental results indicate that the proposed function is superior to the state-of-the-art functions under radiometric variations. In addition, the proposed function is faster than recently developed functions, such as the adaptive normalized cross correlation, a mutual information-based function, and support local binary pattern.</P>

Lithium Chloride-Imidazolium Chloride Melts for the CouplingReactions of Propylene Oxide and CO2

Ly Vinh Nguyen,이보라,이현주,김훈식,강민정,Dinh Quan Nguyen,Je Seung Lee*,류설 대한화학회 2008 Bulletin of the Korean Chemical Society Vol.29 No.1

A series of lithium chloride-imidazolium chloride (LiCl-[imidazolium]Cl) melts were prepared and their catalytic activities were evaluated for the coupling reactions of propylene oxide and CO₂. At the constant mole of LiCl, the catalytic activities of LiCl-[imidazolium]Cl melts increased with increasing molar ratio of [imidazolium]Cl/LiCl up to 2, but thereafter decreased rapidly. The variation of alkyl groups on the imidazolium ring showed a negligible effect on the catalytic activity, but the number of alkyl groups present on the imidazolium cation exerts a pronounced effect. Catalysis and electrospray ionization tandem mass spectral analysis results of LiCl-[imidazolium]Cl melts imply that the activity of the melt is strongly related to the amount of LiCl2- generated from the melt.

ROBUST TRAFFIC LIGHT DETECTION AND CLASSIFICATION UNDER DAY AND NIGHT CONDITIONS

Phuc Manh Nguyen,Vu Cong Nguyen,Son Ngoc Nguyen,Linh My Thi Dang,Ha Xuan Nguyen,Vinh Dinh Nguyen 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10

Recently, traffic light detection and classification systems have been studied and developed to build an autonomous car by many research institutes, universities, and companies. However, the results of existing traffic light detection systems are still not stable under day and night conditions. It is difficult to detect the location of traffic light due to their small size. Moreover, traffic lights’ shapes are also similar to advertisement lights in a city road. Therefore, this paper proposed a new approach to improve the performance of existing traffic light detection systems by using the benefits of hand-crafted features and deep learning techniques. Experimental results show that the proposed system obtained the detection rate of 80% under night conditions, while the color-based density method only got the detection rate of 50.43% under night conditions.